This leads directly into a dilemma: while big-data-driven AI shows exciting results in many different areas, centralized data processing leads to concerns about the security and the privacy of such systems and is not in line with the privacy-by-design principle stipulated in the European Union General Data Protection Regulation [1]}. To counter this dilemma, the technique of federated learning (FL), as proposed by H. Brendan McMahan, et al. [2]}, [3]}, [4]}, [5]}, can be used.
The goal is to learn machine-learning models on data gathered on multiple private devices, while at the same time keeping all this re-training data private. For this purpose, federated learning proposes a decentralized optimization process. As shown in Figure REF , in this way, it ensures user privacy by training on any private data locally, on-device, instead of sending private data to central servers. Each of the clients that are involved in re-training then sends a retrained model to the central server, and the central server updates its global model based on these user models, while all the clients' private data is kept on-device. In an optimal setup, the clients and the central server keep an appropriate communication frequency, maintaining a balance between communication cost and model performance [6]}, [7]}.
